Search Results (511)

As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km³/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions. Full article

Cement-based batteries (CBBs) are an emerging category of multifunctional materials that combine structural load-bearing capacity with integrated electrochemical energy storage, enabling the development of self-powered infrastructure. Although previous reviews have explored selected aspects of CBB technology, a comprehensive synthesis encompassing system architectures, material strategies, and performance metrics remains insufficient. In this review, CBB systems are categorized into two representative configurations: probe-type galvanic cells and layered monolithic structures. Their structural characteristics and electrochemical behaviors are critically compared. Strategies to enhance performance include improving ionic conductivity through alkaline pore solutions, facilitating electron transport using carbon-based conductive networks, and incorporating redox-active materials such as zinc–manganese dioxide and nickel–iron couples. Early CBB prototypes demonstrated limited energy densities due to high internal resistance and inefficient utilization of active components. Recent advancements in electrode architecture, including nickel-coated carbon fiber meshes and three-dimensional nickel foam scaffolds, have achieved stable rechargeability across multiple cycles with energy densities surpassing 11 Wh/m². These findings demonstrate the practical potential of CBBs for both energy storage and additional functionalities, such as strain sensing enabled by conductive cement matrices. This review establishes a critical basis for future development of CBBs as multifunctional structural components in infrastructure applications. Full article

In the task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. This article proposes a novel method for PolSAR image classification that combines channel-wise convolutional local perception, detachable self-attention, and a residual feedforward network. Specifically, the proposed method comprises several key modules. In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. The local residual connections further enhance these extracted features, providing more discriminative information for subsequent processing. Additionally, the detachable self-attention mechanism plays a pivotal role: it facilitates effective interaction between local and global information, enabling the model to comprehensively perceive features across different scales, thereby improving classification accuracy and robustness. Subsequently, replacing the conventional feedforward network with a residual feedforward network that incorporates residual structures aids the model in better representing local features, further enhances the capability of cross-layer gradient propagation, and effectively alleviates the problem of vanishing gradients during the training of deep networks. In the final classification stage, two fully connected layers with dropout prevent overfitting, while softmax generates predictions. The proposed method was validated on the AIRSAR Flevoland, RADARSAT-2 San Francisco, and RADARSAT-2 Xi’an datasets. The experimental results demonstrate that the proposed method can attain a high level of classification performance even with a limited amount of labeled data, and the model is relatively stable. Furthermore, the proposed method has lower computational costs than comparative methods. Full article

Pedicle screw fixation is a critical technique for stabilizing lumbar fractures in canines, yet the biomechanical implications of insertion angles remain underexplored. This study aims to identify optimal screw trajectories by analyzing stress distribution and deformation patterns in beagle lumbar segments (L6-L7) using finite element analysis (FEA). A 3D finite element model was reconstructed from CT scans of a healthy beagle, incorporating cortical/cancellous bone, intervertebral disks, and cartilage. Pedicle screws (2.4 mm diameter, 22 mm length) were virtually implanted at angles ranging from 45° to 65°. A 10 N vertical load simulated standing conditions. Equivalent stress and total deformation were evaluated under static loading. The equivalent stress occurred at screw–rod junctions, with maxima at 50° (11.73 MPa) and minima at 58° (3.25 MPa). Total deformation ranged from 0.0033 to 0.0064 mm, with the highest at 55° and the lowest at 54°. The 58° insertion angle demonstrated optimal biomechanical stability with minimal stress concentration, with 56–60° as a biomechanically favorable range for pedicle screw fixation in canine lumbar fractures, balancing stress distribution and deformation control. Future studies should validate these findings in multi-level models and clinical settings. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Sunflower is one of the four most important oilseed crops in the world, and its edible oil is of high nutritional quality. However, the molecular regulatory mechanism of oil accumulation in sunflowers is still unclear. In this study, we selected two inbred lines with significant differences in oleic acid content and similar agronomic traits: the high oleic acid content (82.5%) inbred line 227 and the low oleic acid content (30.8%) inbred line 228. Sunflower seeds were selected for transcriptome experiments at 10, 20, and 30 days after full bloom (DAFB). There were 21, 225, and 632 differentially expressed genes (DEGs) identified at the three times, respectively. The Gene Ontology (GO) analysis showed that DEGs from two sunflower cultivars at three stages were significantly enriched in the activities of omega-6 fatty acid desaturase and glucosyltransferase. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that at 10, 20, and 30 DAFB, DEGs were significantly enriched in the unsaturated fatty acid synthesis pathway, glutathione metabolism pathway, and pyruvate metabolism pathway. Through mapping analysis of GO in the KEGG pathway, it was found that the omega-6 fatty acid desaturase gene FAD6/FAD2, diacylglyceroyltransferase gene DGAT, glycerol-3-phosphate acyltransferase gene GPAT, and long-chain acyl-CoA synthase gene LACS may play important roles in regulating sunflower oleic acid content. Our research provides candidate genes and a research basis for breeding high oleic sunflowers. Full article

This study aims to evaluate the feasibility and accuracy of 3D printing-guided endodontics in the maxillary teeth of dogs. CT data from a Beagle dog were processed to create a 3D model of the maxilla, and virtual root canal pathways were established using SOLIDWORKS software (version 29.0.0.5028). Guided endodontic templates were 3D printed and tested in vitro on 20 maxillary teeth (excluding the third molars), with 36 root canals treated using both guided and conventional methods. Results indicated that 3D printing-guided endodontics provided accurate root canal pathways, with minimal deviations in length (average 3.08 ± 1.75%) and angular alignment (average 2.06° ± 0.5°) compared to conventional methods. This research represents a significant step forward in the application of 3D printing technology in veterinary endodontics, offering a promising alternative to traditional methods for treating complex dental conditions in dogs. Full article

This study used microscopy-based quantitative enumeration to investigate the effects of large-scale Sargassum fusiforme cultivation on coastal water quality and phytoplankton communities. Data from April (cultivation period) and June (non-cultivation period) in 2018 and 2019 showed that cultivation increased pH and dissolved oxygen (DO). It also reduced nitrate–nitrogen (NO₃–N), nitrite–nitrogen (NO₂–N), phosphate–phosphorus (PO₄–P), total phosphorus (TP), and silicate–silicon (SiO₃–Si) concentrations. These changes indicate improved coastal water quality from S. fusiforme cultivation. Nutrient levels rose again during the non-cultivation period. This suggests that water purification decreased without cultivation. Cultivation also lowered the dominance of Skeletonema costatum. This led to a more diverse and stable phytoplankton community. Microscopic observation is valuable for quantifying larger phytoplankton species, and plays an important role in ecological monitoring. These findings provide insights for sustainable aquaculture and ecological restoration. Full article

With the increasing load and renewable energy capacity in interconnected power grids, the system voltage stability faces significant challenges. Tie-line transmission power is a critical factor influencing the voltage stability margin. To address this, this paper proposes a fully data-driven global sensitivity calculation method for the tie-line power-voltage stability margin, aiming to quantify the impact of tie-line power on the voltage stability margin. The method first constructs an online estimation model of the voltage stability margin based on system measurement data under ambient excitation. To adapt to changes in system operating conditions, an online updating strategy for the parameters of the margin estimation model is further proposed, drawing on incremental learning principles. Subsequently, considering the source–load uncertainty of the system, a global sensitivity calculation method based on analysis of variance (ANOVA) is proposed, utilizing online acquired voltage stability margin and tie-line power data, to accurately quantify the impact of tie-lines on the voltage stability margin. The accuracy of the proposed method is verified through the Nordic test system and the China Electric Power Research Institute (CEPRI) standard test case; the results show that the error of the proposed method is less than 0.3%, and the computation time is within 1 s. Full article

Inverse lithography technology (ILT) based on the gradient descent (GD) algorithm, which is a classical local optimal method, can effectively improve the lithographic imaging fidelity. However, due to the low-pass filtering effect of the lithography imaging system, GD, although able to converge quickly, is prone to fall into the local optimum for the information in the corner region of complex patterns. Considering the high-frequency information of the corner region during the optimization process, this paper proposes a resolution layering method to improve the efficiency of GD-based ILT algorithms. A corner-rounding-inspired target retargeting strategy is used to compensate for the over-optimization defect of GD for inversely optimizing the complex pattern layout. Furthermore, for ensuring the manufacturability of masks, differentiable top-hat and bottom-hat operations are employed to improve the optimization efficiency of the proposed method. To confirm the superiority of the proposed method, multiple optimization methods of ILT were compared. Numerical experiments show that the proposed method has higher optimization efficiency and effectively avoids the over-optimization. Full article

China generates substantial construction and demolition (C&D) waste, owing to rapid urbanization. However, the resource utilization rate of C&D waste remains low. This work is devoted to promoting the application of C&D waste in reinforced soil structures. In this research, the physical and mechanical properties of C&D waste recycled aggregate, biaxial geogrids and triaxial geogrids were first clarified. Then, a series of pullout tests were carried out based on the large-size pullout test setup. With the help of macroscopic indicators, including pullout resistance, horizontal displacement and interface friction coefficient, the effects of normal stress, pullout rate and reinforcement type on the characteristics of the reinforcement–C&D waste recycled aggregate interface were clarified. The test results show that normal stress has the greatest influence on pullout resistance. The pullout rate has the lowest effect on pullout resistance. In addition, the interface effect between the triaxial geogrid and the C&D waste recycled aggregate is more significant than that in biaxial geogrid–C&D waste recycled aggregate. The interface friction angle of triaxial geogrids is 18.1% higher than that of biaxial geogrids (11.6° vs. 9.82°), correlating with an enhanced particle interlocking mechanism. Full article

This study assessed the environmental risks and regional variations associated with using titanium gypsum in road construction. It revealed that the conventional HJ/T299-2007 leaching method underestimates heavy metal leaching rates from titanium gypsum by approximately 1%, potentially leading to an underestimation of environmental risks. Further analysis indicated that Pb, Ni, As, Cd, and Zn leach from titanium gypsum road materials to varying extents, while Mn poses a notable exceedance risk with an 11% probability of surpassing limits and a maximum exceedance factor of 1.8. Significant disparities in regulatory thresholds for titanium gypsum pollutants were observed among 11 provinces along the Yangtze River, with the highest threshold (Qinghai) nearly five times greater than the lowest (Jiangxi). Rainfall was identified as a key contributor to these regional differences. The findings suggest that traditional assessment methods underestimate titanium gypsum risks and highlight the need for enhanced national solid waste evaluation frameworks. Additionally, given the substantial regional risk variations, differentiated management strategies are recommended. Full article

Remote-sensing image-change detection is indispensable for land management, environmental monitoring and related applications. In recent years, breakthroughs in satellite sensor technology have generated vast volumes of data and complex scenes, presenting significant challenges for change-detection algorithms. Traditional methods rely on handcrafted features, which struggle to address the impacts of multi-source data heterogeneity and imaging condition differences. In this context, technology based on deep learning has made substantial breakthroughs in change-detection performance by automatically extracting high-level feature representations of the data. However, although the existing deep-learning models improve the detection accuracy through end-to-end learning, their high parameter count and computational inefficiency hinder suitability for real-time monitoring and edge device deployment. Therefore, to address the need for lightweight solutions in scenarios with limited computing resources, this paper proposes an attention-based lightweight remote sensing change detection network (ABLRCNet), which achieves a balance between computational efficiency and detection accuracy by using lightweight residual convolution blocks (LRCBs), multi-scale spatial-attention modules (MSAMs) and feature-difference enhancement modules (FDEMs). The experimental results demonstrate that the ABLRCNet achieves excellent performance on three datasets, significantly enhancing both the accuracy and robustness of change detection, while exhibiting efficient detection capabilities in resource-limited scenarios. Full article

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin. Full article